Conventional engineering models are exercised using a predefined set of input data. Such input data sets are commonly constructed of specific and potentially changing sets of facets, such as location, physical properties, loading, etc. The ability to easily access, modify, and recombine these facets is one of the paramount objectives in a good model. Modeling involves the iterative analysis of a multitude of “What if?” scenarios that evaluate different facets of the data. Conventional iterative modeling environments typically consist of the time consuming tasks of 1) copying a multitude of datasets or databases into a computer work area or storage area, 2) making necessary modifications to the selected data elements included in these datasets, and 3) re-executing the model. There are two primary disadvantages to this type of modeling environment: the multiple data duplication operations may create data errors, and the data duplication does not optimize computer storage space.
For example, a model representation of a water distribution network may include incorrectly entered data regarding a particular element. Such network data may then be copied and more data added to represent a post-development condition of the network, thus forming a new “What If?” scenario. Upon discovery of the original error, any set of data that used the original erroneous entry would then require modification and assurance that it was edited correctly.
Furthermore, data used in engineering applications is by its nature unitized, i.e. each input value is associated with a corresponding measuring unit. Values without defined measuring units do not provide sufficient information to a modeling application to be evaluated appropriately. Traditionally, this problem has been solved by forcing the users of an engineering application to use a predefined set of units. Such a limitation can be constraining in that when application units do not match the data units provided by a database, the user thus needs to perform unit transformation outside of the application, and is required to know an exact mapping relationship between the application units and the units of the input data. Such a unit transformation process may introduce errors, thus invalidating modeling results. Such errors are typically hard to detect and often go unnoticed.
Finally, forcing the input data units to match application units introduces strong coupling between the application and input data. If the application units need to be changed, existing input data may need to be changed as well, further introducing potential errors into the data.
There remains a need, therefore, for a system for managing engineering modeling data that reduces the number of errors carried forward in data duplication in the model. There remains a further need for a system that allows unitized data to be readily manipulated and converted into different unit formats.